This concern is surely in part due to non-robust training techniques, and a plethora of methods improve classifier robustness [1]}, [2]}, [3]}. However, datasets play an important role in controlling the extracted bias as well. For instance, if correct answers in a question-answering task are significantly shorter than incorrect ones, classifier training should not use answer length as a cue. Although this seems reasonable, for audio-visual scene aware dialog, [4]} find for example that in many cases the question alone is sufficient to generate a scene-aware dialog response, avoiding the need to look at the video. Hence, in order to assess the suitability of a classifier, we need to understand how much it relies on different data modalities.
